Political stability and economic policy uncertainty can be key determinants of sovereign debt dynamics, and we show how they can be incorporated in debt sustainability analysis. We distinguish between short-term ambiguity and long-term uncertainty about political risk factors, and using a combination of narrative scenarios and calibrated probabilistic scenarios we obtain a comprehensive heatmap of high-risk debt dynamics. We use Italy as an interesting case study and demonstrate a “red shift” in the assessment of vulnerabilities when accounting for political risks. Ignoring these risks can lead to excessive optimism and wrong decisions.

When lenders
such as the International Monetary Fund or the European Stability Mechanism want
to assess whether a country meets the criteria for receiving international
assistance, they carry out a debt sustainability analysis (DSA). This involves
debt simulations and scenarios that evaluate the likelihood of countries
meeting their future obligations. The Greek debt crisis, however, exposed the
drawbacks of traditional debt sustainability analysis. During crisis episodes
uncertainty is high, and therefore focusing on average dynamics, or on a few scenarios,
can conceal potential risks. DSA applies to crisis countries only, but an early warning system identifying vulnerabilities
is relevant for all countries. A more general, less stringent, debt vulnerabilities analysis (DVA)
could be used to assess a country’s debt management policies to identify
vulnerabilities, without leading immediately to policy consequence.

DVA would not
carry the significant connotations of DSA. It would have less stringent criteria for
raising red flags, and would not lead immediately to policy consequences. For
instance, it could be used to spot when a country has debt that is
non-decreasing from very high levels, even though that country might pass the
DSA test. The Dutch State Treasury Agency evaluates its public debt management
practices every three years, even though its debt-to-GDP ratio is only 50%. The
agency carries out a comprehensive analysis prior to a political review,
including an evaluation of vulnerabilities, and the implications for policy are
transmitted to the Dutch parliament by the finance minister (the 2019
evaluation is available here).

Such broader analysis could, in particular, account for political
risks that are currently used to guide expert judgment by the institutions, but
are not part of DSA. The IMF makes references to political risks and policy
uncertainty in its Article IV Consultation reports,[1]
ESM uses governance and/or political risk ratings in its Sovereign
Vulnerabilities Index, and ECB uses such ratings to generate a heat map,
classifying RED countries in the bottom ratings tercile, GREEN in the top
tercile, and YELLOW in the middle. Such broad-brush treatment of political
risks is useful, but unlikely to be effective.

Political instability
and economic policy uncertainty can be key determinants of sovereign debt
dynamics, but are not captured adequately, with quantitative rigor, by
traditional DSAs. When the IMF, the ECB or the ESM carry out DSAs, they take
into account risk factors including the country’s fiscal consolidation path,
GDP growth and financial assumptions relating to the sovereign bond yields. They
also consider debt aging costs, macro (bank) stress tests, inflation shocks,
structural shocks, contingent liabilities and privatisation receipts. We argue
that political risk factors can also be quantified, and should be part of debt
analysis.

Our suggestion
becomes attractive because the systematic quantification of political risks has
been receiving increasing attention, driven in part by the compilation of
databases that facilitate cross-sectional studies. Such databases include the
Ifo World Economic Survey-WES (25 years of semi-annual data for 66 countries),
the World Bank (25 years of annual data for 214 countries), the ICRG index (40
years of annual data for up to 140 countries) and the Dallas Fed’s Economic
Policy Uncertainty index (25 years of monthly data for 21 countries). Impetus has
also been created by theoretical models and empirical evidence that the markets
price political risks.

Building on these advances, political risks
can be incorporated in DSA (and DVA) and can materially affect the conclusions.
We first identify (see Gala et al.
2018) two key quantifiable dimensions of political risk, and second distinguish
between short-term ambiguity about the political factors that cannot be
measured, and long-term risks that are modelled probabilistically. Third, we use
a combination of narrative scenarios about the short-term ambiguity, and
calibrated probabilistic scenarios for long-term risks (and Zenios et al. 2019), to obtain a comprehensive
heatmap of high-risk debt dynamics. As an example of what happens when
political risks are included, we look at recent developments in Italy (see
below).

Short-term
political ambiguity and long-term uncertainty

To incorporate political risks in DSA, we are
faced with the problem of uncertainty specification, which has been daunting
economists for a very long time (see for example Knight, 1921, and Arrow,
1951). We need to account for short-term ambiguity (ie which government wins
the election, what policies will they implement, will a country be able to
follow an adjustment programme?) and for the long-term volatility towards a
well estimated expected future state, if we think that such an equilibrium state
exists. In general, it is not possible to estimate reliably an election outcome
or what policies a new government will pursue.

We adopt narrative scenarios for variables
with ambiguous immediate outcomes, to see what the bad outcomes might be, and
calibrated probabilistic scenarios for long-run uncertainty to estimate
appropriate risk metrics. We run the DSA model for a range of plausible values
for the critical variables that are affected by political event, such us
government surplus and country GDP growth. For the long-run risks, we calibrated
scenarios of economic, fiscal and financial variables, accounting for political
effects. With this approach we identify values for ambiguous variables with
high probability of bad outcomes, so that they can be avoided. The result is a
comprehensive heatmap of high-risk debt dynamics, with quantile optimisation
for those aspects of the problem that are amenable to scenario calibration, and
identification of narrative scenarios with bad outcomes that must be avoided,
for the ambiguous aspects.

Italy
as a case study

We applied our model to the 2019
budget agreement between the Italian government and the European
Commission. We assess whether Italy can stay on a non-increasing debt path with
gross financing needs below an IMF-specified threshold of 20% of GDP, and
demonstrate the material effects of political risks (Note: our assessment
criteria are less stringent than those of official DSAs).

We start with a scenario tree covering GDP
growth, the primary balance and the risk-free rate of euro-area 5-year AAA
rated sovereigns, but without political variables. The scenario tree was
calibrated to Italy’s conditions and observed market data, using historical
volatilities and correlations. To the scenarios of risk-free rates, the model
added premia capturing the response of borrowing rates to debt levels.

A significant short term political risk was
the fiscal stance of the new Italian government following the 2018 elections.
We parametrically change growth and primary balance projections to cover
plausible outcomes, and evaluate, using the calibrated long-term scenario trees,
the likelihood of debt stocks and gross financing needs staying within the
thresholds. The result is a heatmap that shows the likelihood of debt dynamics
remaining within the thresholds for a wide range of the ambiguous variables. We
use the model to draw the heatmap and assess Italian debt dynamics under three
narrative scenarios: (i) no policy change; (ii) the new Italian government
achieves its growth and surplus projections; and (iii) Italy reaches the
targets in the agreement negotiated with the European Commission. For each
narrative scenario we assess if its outcomes would violate the thresholds, and,
therefore, must be avoided.

Figure 1 shows the heat map, with dark green
denoting an extremely low probability (0.01) of unsustainable dynamics, and red
denoting a very high probability (0.85). Note that for a wide range of
combinations of GDP growth and primary balance, the dynamics are unsustainable
with very high probability. Italy is clearly vulnerable. The map also locates
our narrative scenarios: ‘IMF’ denotes projections from the IMF World Economic
Outlook report for 2018. Under our model calibration, and without any change in
policy, the debt dynamics are highly likely to be unsustainable. ‘Pre- agreement’
corresponds to the Italian government targets before the budget agreement with
the European Commission. It improves on the previous policy but is still in the
red zone. ‘Post-agreement’ presents further improvements, shifting Italy into
the yellow zone, with a probability of 0.55 for sustainable dynamics. The wisdom
of a policy with a 0.55 chance of achieving its objectives is questionable and
additional fiscal effort would be needed to increase to 0.85 the probability of
remaining within the thresholds (green). Using the model, we estimate that with
a total fiscal effort of 3.5% of GDP over twelve years, capped at 0.3% per
annum, Italy can reach this target. This finding is in agreement with Sapir
(2018) that Italy should have been running consistently a higher primary
surplus to avoid finding itself in its current predicament, although our
estimates for the extra effort are lower.

[Insert Figure 1]

Source: Authors calculations using the
model of Zenios et al (2019).

We then incorporated long-term political
risks. We generated a new scenario tree with political stability and economic
policy confidence state variables, calibrated to Italy’s volatile political
variables around estimated long-term trends. To calibrate the political state
variables, we assume that they converge long-term to their historical averages
of 4.5 (out of 10) for stability and 15.5 (out of 100) for policy. We also
estimate volatilities from the historical ratings for Italy, namely a standard
deviation of 1 for stability and 11 for economic policy confidence. The
political variables are correlated with growth, primary balance and interest
rates, with historical correlations from -0.44 to 0.75, respectively.
Regression estimates of the bond yield sensitivities to these factors are then
added to the refinancing costs scenarios, adjusted according to the endogenous
debt risk premium. We re-run the model including political risk premia and
redraw the heatmap (Figure 2).

[Insert Figure 2]

Source: Authors calculations using the
model of Zenios et al (2019).

From this, a marked increase in the area that
denotes a high probability of unsustainable dynamics can be seen. With
political vulnerabilities taken into account, more combinations of growth and
primary surplus are highly likely to violate the thresholds. Under our model,
the agreement with the European Commission, which was estimated to have a
slightly better than 0.50 chance of success, would only have a 0.15 chance of
success when political risks are accounted for. The additional fiscal effort
that restores sustainability with probability of 0.85, is now borderline light green,
with a 0.65 to 0.55 chance of success. Clearly, ignoring the political risks
can lead to excessive optimism and wrong decisions.

[1] See,
for instance, the 2018 Financial Stability Report and
the 2018 Global Outlook Reports. Such references appear twenty-six times in the
2016 Article IV report for Greece, twelve times in the 2018 report, and four
times in 2019.